Article(id=1314921942864457779, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314921940238828053, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1747152000000, receivedDateStr=2025-05-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791431421247, onlineDateStr=2026-10-08, pubDate=1768838400000, pubDateStr=2026-01-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791431421247, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791431421247, creator=13701087609, updateTime=1791431421247, updator=13701087609, issue=Issue{id=1314921940238828053, tenantId=1146029695717560320, journalId=1234093305789726721, year='2026', volume='46', issue='1', pageStart='1', pageEnd='592', issueExtLink='null', onlineDate='null', pubDate='1768838400000', pubDateStr='2026-01-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791431420621, creator='13701087609', updateTime=1791431552947, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314922495317210104, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314921940238828053, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314922495317210105, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314921940238828053, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=20, endPage=28, ext={EN=ArticleExt(id=1314921943057395764, articleId=1314921942864457779, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Dynamic prediction model for mercury flux at the water-air interface: A case study of Changshou Lake, columnId=null, journalTitle=China Environmental Science, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Quantifying Hg flux in lake systems remains challenging due to the synergistic influences of multiple environmental drivers. To address this, we investigated Changshou Lake, an artificial reservoir situated in a karst region, through an integrated approach that included field sampling, path analysis, and machine learning, to elucidate the mechanisms of Hg flux exchange. The results indicated that Hg flux was jointly regulated by multiple factors via both direct and indirect pathways. Water Hg concentration (0.897), UV radiation (0.463), and solar radiation (0.446) were identified as exerting dominant direct effects on the production of dissolved gaseous mercury (DGM, Hg⁰). In contrast, the influence of wind speed on Hg flux was observed to be stronger through its indirect enhancement of solar radiation (0.252) than through direct diffusion (0.197). A hybrid machine-learning prediction mode [0.9 Gradient Boosting Regression (GBR) + 0.05 Random Forest (RF) + 0.05Support Vector Regression (SVR)] was developed. This model was shown to improve generalization ability and stability while maintaining predictive accuracy, enabling dynamic prediction of Hg flux under the coupling of multiple lake parameters. The approach provides decision support for regional Hg risk assessment and management.

, authors=Wei-yang BAI^{1, 2, *}, Rui LIU¹, Hao-hao JIANG¹, authorsList=Wei-yang BAI, Rui LIU, Hao-hao JIANG, authorCompany=null, correspAuthors=Wei-yang BAI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314921944131137590, articleId=1314921942864457779, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=水-气界面汞通量动态预测模型构建——以长寿湖为例, columnId=1314921943346802741, journalTitle=中国环境科学, columnName=岩溶关键带重金属污染成因, runingTitle=null, highlight=null, articleAbstract=

针对湖泊系统汞通量估算受多环境变化因素协同影响，不确定性明显，以喀斯特岩溶的人工水库长寿湖为研究对象，综合运用野外采样，通径分析与机器学习方法来揭示汞通量交换机制．研究发现：汞通量受多因子直接/间接的协同调控．其中，水体汞浓度（0.897）、紫外线强度（0.463）和光照强度（0.446）的直接效应主导着水体溶解气态汞Hg⁰（DGM）生成，而风速通过光照强度（0.226）的间接影响强于其直接扩散作用（0.197）．基于机器学习构建的汞通量预测混合模型（0.9GBR（梯度提升机）+0.05RF（随机森林）+0.05SVR（支持向量机回归）），在保持预测精度的同时提升鲁棒性和稳定性，实现湖泊多参数耦合下汞通量的动态预测，为区域汞环境风险评估与管控提供决策支持．

, authors=白薇扬^{1, 2, *}, 柳睿¹, 江豪浩¹, authorsList=白薇扬, 柳睿, 江豪浩, authorCompany=null, correspAuthors=白薇扬, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=Zql5frJPjZYekdpCz0S97g==, magXml=jtVZ0QMcUSn3SruGxczD9A==, pdfUrl=null, pdf=6VYfdFrXobFJs5D7sM8vwA==, pdfFileSize=1503483, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=SCrG04JG0Vnkvr0zTk5HHg==, mapNumber=null, fund=null)}, authors=[Author(id=1314921946966487105, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314921942864457779, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=baiwy@cqut.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=1, authorType=1, ext={EN=AuthorExt(id=1314921947041984580, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314921942864457779, authorId=1314921946966487105, language=EN, stringName=Wei-yang BAI, firstName=Wei-yang, middleName=null, lastName=BAI, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2, *}, address=^1.College of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing 400054, China
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^2.西南大学资源环境学院，重庆 400715, bio={"content":"

白薇扬（1980-），女，重庆人，副教授，博士，主要研究方向为环境重金属污染控制．发表论文20余篇．baiwy@cqut.edu.cn.

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白薇扬（1980-），女，重庆人，副教授，博士，主要研究方向为环境重金属污染控制．发表论文20余篇．baiwy@cqut.edu.cn.

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